Senior Data Engineer

Candidates must possess mastery of SQL and strong data modeling instincts, with comfort in modeling diverse business domains. Proficiency in Python is required, along with a proven ability to build data platforms from scratch, including tool selection and standard setting in a fast-paced environment. Fluency with ELT and transformation frameworks like dbt or SQLMesh, and orchestrators such as Airflow or Dagster, is essential. Pragmatic engineering habits including testing, version control, PR reviews, incident management, CI/CD, documentation, and data observability are necessary. Excellent cross-functional communication skills are needed to translate business needs into data products. Experience with ingest tools like Fivetran, Airbyte, or Stitch, and reverse ETL, along with familiarity with common SaaS/DB sources, is expected. Hands-on experience with Snowflake or Databricks and performance/cost tuning at scale, as well as building transformations with dbt or SQLMesh and managing semantic layers for BI tools like Looker, Omni, or Mode, are considered advantageous.